15,443 research outputs found
Scalable Group Level Probabilistic Sparse Factor Analysis
Many data-driven approaches exist to extract neural representations of
functional magnetic resonance imaging (fMRI) data, but most of them lack a
proper probabilistic formulation. We propose a group level scalable
probabilistic sparse factor analysis (psFA) allowing spatially sparse maps,
component pruning using automatic relevance determination (ARD) and subject
specific heteroscedastic spatial noise modeling. For task-based and resting
state fMRI, we show that the sparsity constraint gives rise to components
similar to those obtained by group independent component analysis. The noise
modeling shows that noise is reduced in areas typically associated with
activation by the experimental design. The psFA model identifies sparse
components and the probabilistic setting provides a natural way to handle
parameter uncertainties. The variational Bayesian framework easily extends to
more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1
Learning Multimodal Latent Attributes
Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning
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